Recently, a method to estimate incident duration using hybrid Decision Trees based on Quantile Regression [1]} was proposed in [2]}. The GUIDE algorithm (Decision Trees) based on incident characteristics was used for partitioning of the input space. Then, a Quantile Regression model was used in each leaf of the Decision Tree based on traffic variables as predictors.
The approach presented in [2]} is similar to Luo's GUIDE algorithm, but used Quantile Regression models to compute the conditional distribution of the target variable.
The cited work [2]} and the models discussed in our paper share a similarity in using the Quantile Regression model inside each partition, but differ in the way partitions are computed.
Another differentiating aspect is that Decision Tree based on the Classification and Regression Trees (CART) algorithm [5]} was used in our models, whereas the GUIDE algorithm was used for partitioning in [2]}. In [7]}, an efficient algorithm for calculating SHAP values for Piecewise linear Decision Trees and ensemble models based on this, was proposed. The cited paper proved that time complexity of an algorithm to compute SHAP values for Piecewise linear DTs would be bounded by \(O(MLD^2)\) , where \(M\) , \(L\)  and \(D\)  represent the number of features, number of leaf nodes and depth of the tree.
